Search Results (22,512)

The digital transformation of entrepreneurial finance has progressed beyond basic FinTech adoption toward the deeper digitalization of financial processes and the integration of artificial intelligence (AI). Yet, firms, particularly non-financial SMEs, vary substantially in their ability to convert these technologies into superior entrepreneurial, market, and financial outcomes. This study develops and tests a capability-based model explaining how FinTech-enabled financial process digitalization (FPD) and AI use shape entrepreneurship by influencing entrepreneurial performance outcomes. In line with current developments in digital finance, AI use is conceptualized as an embedded and complementary feature of FinTech-enabled financial process digitalization rather than an independent technological category. Drawing on the resource-based view and behavioral finance, we propose digital financial capability (DFC) as a central mechanism through which FinTech-enabled digitalized finance creates value, while credit fear is conceptualized as a behavioral constraint that limits entrepreneurial outcomes. We further posit customer satisfaction as a market-facing outcome linking financial capabilities to firm performance. Using survey data from 318 non-financial SMEs operating in Greece and applying Partial Least Squares Structural Equation Modeling (PLS-SEM), the findings show that FPD and AI use significantly enhance DFC, which in turn increases customer satisfaction and entrepreneurial performance. In addition, financial process digitalization reduces credit fear, thereby mitigating its negative impact on entrepreneurial performance. By shifting the focus from technology adoption toward AI-supported capability development within digitally enabled financial processes and behavioral mechanisms, this study advances FinTech and entrepreneurship research and offers actionable insights for managers and policymakers seeking to leverage digital finance for sustainable entrepreneurial value creation. Full article

Social networks such as Twitter have grown rapidly and are now flooded with sarcastic comments, both in text and in images. Detecting sarcasm in multimodal data has significant social value and is attracting increasing research attention. However, most studies overlook the role of sentiment, even though sentiment information in text is closely linked to clues of sarcasm. Additionally, few consider how text and images align semantically. To address these issues, we propose a sentiment-guided mutual learning network (SGMLN) for multimodal sarcasm detection. SGMLN utilizes sentiment information to inform the combination of text and image features, and employs mutual learning to facilitate knowledge sharing among classifiers. We design a sentiment-guided attention layer that injects sentiment into both modalities, producing features that capture sarcasm more effectively. Sentic-BERT extracts sentiment-aware vectors from text, using word-level sentiment as a mask. In mutual learning, a logistic distribution function measures differences between classifiers, improving knowledge transfer between modalities. This step boosts multimodal understanding and model performance. By introducing sentiment-aware representations and semantic alignment, SGMLN bridges the gap between text and images, making them more consistent. Experiments on public datasets demonstrate that our model is effective and outperforms alternatives. Full article

Traffic variability along heavily congested signalised urban corridors undermines roadway safety, reduces energy efficiency, weakens operational reliability, and can hinder emergency response. Although many simulation-based studies have examined the impacts of Autonomous Vehicles (AVs), relatively few have combined high-resolution congestion observations with link-level microscopic calibration in a real urban network, particularly when evaluating implications for emergency mobility. This study develops and calibrates a microscopic Aimsun traffic simulation model for the Atakum district of Samsun, Türkiye, using a 10 min Google Traffic Index (GTI) observation stream converted into a four-level ordinal congestion scale. The calibration process began with an origin–destination (OD) matrix derived from 2020 traffic counts and was refined through link-level GTI synchronization, iterative OD scaling on mismatched corridors, and signal retiming at key intersections. GTI was validated as an ordinal congestion proxy through both categorical agreement and volumetric consistency, achieving 83% class agreement and GEH values below 5 for more than 90% of links. Five AV penetration scenarios (0%, 25%, 50%, 75%, and 100%) were simulated under peak-hour conditions. Network performance was evaluated using delay, stop time, mean speed, throughput, missed turns, and total journey time, while emergency mobility was assessed along a representative ambulance corridor on Atatürk Boulevard using seconds per kilometre. The results indicate that increasing AV penetration improves flow stability more clearly than nominal capacity. Mean speed increased from 36.2 to 39.2 km/h, delay and stop time declined steadily, and throughput remained nearly constant at 22.2–22.5 thousand vehicles/h. Along the ambulance corridor, travel time improved by 11.5%, from 112.4 to 99.4 s/km, between the baseline and full automation scenarios. These findings provide scenario-based evidence that, within a calibrated signalised urban network, increasing AV penetration can enhance operational stability and emergency response efficiency. More broadly, the study demonstrates the practical value of integrating GTI-based congestion observations with microscopic simulation for AV impact assessment in real urban networks. Full article

Arid Central Asia (ACA) is a major source of atmospheric dust in the Northern Hemisphere; however, the evolutionary models and driving mechanisms of Holocene aeolian activity in this region remain debated. Based on 13 reliable AMS ¹⁴C dates from the Sayram Lake SLM2009 sediment core, this study reconstructs the Holocene sequence in aeolian activity through end-member modeling analysis (EMMA). It evaluates its relationship with regional atmospheric circulation. Four end-members were identified from base to top: EM1, with a modal grain size of 7.58 μm, represents low-energy suspension deposition; EM2 (26.30 μm) reflects lacustrine hydrodynamic processes; while EM3 (52.48 μm) and EM4 (416.86 μm) serve as proxies for regional aeolian activity. The results indicate that aeolian activity was relatively strong during the early Holocene (reaching peaks at 11.7–11.2 and 9.2–8.1 cal ka BP), significantly intensified during the mid-Holocene (7.3–5.3 cal ka BP), and gradually weakened in the late Holocene (since 4.0 cal ka BP). Comparison of the aeolian record from Lake Sayram with Greenland ice cores, North Atlantic ice-rafted debris events, and the GISP2 K⁺ record indicates that variations in aeolian activity in arid Central Asia are closely linked to the Northern Hemisphere climate system. We propose that these variations were primarily modulated by large-scale atmospheric circulation, driven by the synergistic interaction between the Siberian High and the mid-latitude westerlies. Full article

Demand for camel milk products is growing in Morocco and worldwide, creating opportunities to strengthen the livelihoods of populations living in arid regions through the development of camel-based dairy value chains. In addition to their economic potential, such value chains may contribute to sustainability by supporting food systems adapted to arid environments, promoting the use of locally resilient livestock species, and enhancing the socio-economic viability of vulnerable rural communities. This exploratory qualitative study investigates urban consumer behavior related to dairy consumption with a specific focus on the potential integration of camel milk products into local dietary habits. To capture nuanced consumer perspectives, gender-segregated focus-group discussions were conducted in three Moroccan cities using a semi-structured questionnaire on dairy consumption habits. Key factors examined included milk types, product preferences, purchasing locations, consumption frequency and willingness to include camel products in the household diet. The results indicate that camel milk is rarely consumed outside areas where camels are raised. Nevertheless, participants expressed interest in several camel milk-based products, particularly fermented milk and spreadable cheeses. This interest was primarily driven by perceptions of camel milk as a healthy product and by its association with traditional food practices. These findings suggest that expanding camel milk consumption in urban markets could support more sustainable and territorially rooted dairy systems by linking consumer demand with production models suited to dryland conditions. This study indicates promising market opportunities for the development of camel milk products in urban areas, particularly if challenges related to pricing strategies, distribution network, and region-specific supply chains are strategically managed. Full article

This study advances the conceptual and practical scope of social education by integrating Security and Safety Education (SSE) categories into its theoretical foundation. We demonstrate that SSE encompasses multidimensional areas highly relevant to social education and offer a structured competence model to guide curriculum design. Using a mixed-methods approach, 2926 Web of Science publications were analysed through an NVivo Word Frequency Query to identify key domains associated with security and safety. The temporal scope of the corpus (2019–2021) provides a coherent analytical baseline, capturing intensified security and health-related discourse during the COVID-19 period while preceding geopolitical disruptions that could otherwise distort thematic patterns. The results show that security is associated with broad social and geopolitical issues, including food, political, economic, public, national, and international affairs, as well as health and information. In contrast, safety is mainly linked to transport-related concerns, although both domains converge in areas such as health, social, public, national, and information matters. These findings indicate that SSE encompasses multidimensional areas relevant to social education. To support curricular integration, we propose an eMEDIATOR-derived competence model that structures SSE content into measurable, outcomes-based components. Ultimately, this research provides actionable tools to elevate social education and promote active, informed citizenship in times of global uncertainty. Full article

Reconfigurable intelligent surfaces (RIS) have emerged as a promising technology for enhancing wireless communication by intelligently shaping the propagation environment. However, non-line-of-sight (NLoS) blockage between the access point (AP) and user equipment (UE) can still significantly degrade communication performance. This paper investigates the channel degradation caused by NLoS blockage in a single-antenna AP and multi-antenna UE system and proposes a joint power allocation and phase optimization scheme based on RIS and deep reinforcement learning (DRL). Under a composite channel model with direct and RIS-reflected links, the objective is to maximize the weighted sum rate subject to total power constraints, unit-modulus constraints on RIS elements, and quality of service (QoS) requirements. Due to the coupled variables and the non-convex unit-modulus constraint, conventional alternating optimization (AO) and convex approximation methods usually incur high complexity and yield suboptimal solutions. To address this issue, a DRL algorithm based on an Actor–Critic architecture is developed to learn adaptive power allocation and reflection coefficient adjustment policies through interaction with the environment, without requiring full global channel state information (CSI). Simulation results demonstrate that the proposed method achieves higher signal-to-interference-plus-noise ratio (SINR) and throughput while providing faster convergence and better generalization than existing methods. Full article

This study presents a unified mechanics-based framework for evaluating bonded composite patch repairs. Discrete fracture, fatigue, and adhesive responses are transformed into continuous master equations over the design space. Low-order polynomial surfaces model stress intensity and concentration responses, enabling continuous prediction of repair performance without repeated finite-element analyses. A fracture-based repair efficiency index is derived from the analytical master surface. This index quantifies the average reduction in crack-driving force across the domain. Combined with adhesive stiffness and strength, it defines an adhesive-based repair efficiency index (A-REI), providing a direct link between structural response and material properties. The results show that repair effectiveness is strongly influenced by both geometric severity and adhesive properties. Fatigue performance decreases significantly with increasing notch ratio in single-sided repairs. Double-sided configurations maintain consistently higher efficiency. Symmetric reinforcement more effectively reduces stress concentration, with improvements exceeding 40% at intermediate notch ratios. Adhesive selection is governed by stiffness and strength. Structural adhesives achieve significantly higher A-REI values, whereas compliant adhesives contribute negligibly. Overall, repair symmetry controls the magnitude of improvement, while adhesive properties determine performance ranking. This framework provides a clear, practical basis for design and material selection. Full article

This study explores how board characteristics impact corporate cash flow risk in an emerging market setting. While previous research has examined firm risk, crash risk, and earnings quality, there is limited evidence on cash flow risk and its governance factors, especially in developing economies. To fill this gap, this study differentiates between volatility-based and distortion-based measures of cash flow risk and assesses how board attributes influence these aspects. Using a balanced panel of 327 non-financial firms listed in Vietnam from 2013 to 2023, cash flow risk is measured by the rolling five-year volatility of operating cash flows and short-term distortions shown in earnings–cash flow mismatches. To address endogeneity and dynamic persistence, the analysis uses the system generalized method of moments estimator, along with fixed-effects and feasible generalized least squares models for robustness. The findings suggest that board independence, gender diversity, and financial expertise are linked to lower cash flow risk, highlighting the importance of effective monitoring. Conversely, board meeting frequency is positively linked to risk, suggesting that boards tend to increase meeting frequency as a reactive response to heightened uncertainty. Board size and CEO duality do not show consistent effects. Focusing on Vietnam’s institutional context, this study provides evidence that governance mechanisms influence different dimensions of cash flow risk through separate channels, offering valuable insights for enhancing board effectiveness in emerging markets. Full article

Background/Objectives: Craniofacial malformations such as orofacial clefts affect ~1 in 700 births; 40–60% lack clear genetic etiology, and many exhibit asymmetry and variable expressivity unexplained by classical Sonic Hedgehog (SHH) morphogen gradient models. We investigated whether integrated molecular modules linking morphogen signaling with metabolic stress responses may better account for craniofacial developmental outcomes. Methods: Sequential UniProt gene set integration identified 186 candidate craniofacial regulators. STRING network analysis revealed modular architecture. Molecular docking profiled 17 compounds against SMO, CK1δ, PINK1, and TIE2 (control). Pathway reconstruction integrated the SHH–CK1δ–HIF1A–HEY1–PINK1 axis with in-silico-predicted CK1δ phosphorylation sites on SMO (S615, T593, S751), HIF1A (Ser247), and GLI1/2/3 transcription factors. A developmental decision tree mapped affinity profiles to node-specific phenotype hypotheses. Results: CK1δ and PINK1 emerged as candidate nodes coupling morphogen signaling with mitochondrial quality control. Cross-docking showed preferential binding to developmental kinases (CK1δ: −8.34 kcal/mol; PINK1: −8.80 kcal/mol) versus TIE2 control (−6.76 kcal/mol; p < 0.001). Pathway reconstruction suggested that CK1δ-mediated Ser247 phosphorylation of HIF1A disrupts ARNT dimerization, redirecting HIF1A toward ARNT-independent HEY1 induction and consequent PINK1 suppression. Based on computed profiles, node-specific associations were proposed as computational hypotheses: SMO perturbation → midline defects; CK1δ → facial asymmetry/clefting; PINK1 → mandibular hypoplasia. Multi-target compounds (e.g., purmorphamine, taladegib) generated composite phenotype predictions consistent with clinical complexity. Conclusions: This strictly in silico study identifies candidate integrated morphogenic modules whose multi-node perturbation may underlie anatomically specific craniofacial malformation patterns. Node–phenotype associations are prioritized computational hypotheses requiring experimental validation; if confirmed, the framework could inform developmental toxicity assessment, therapeutic design, and reclassification of idiopathic craniofacial anomalies. Full article

As part of a NASA University Leadership Initiative (ULI) program, this work supports the continued development and evaluation of a fatigue-based process window for stress-relieved Ti-6Al-4V specimens produced via laser powder bed fusion (PBF-LB/M). Four-point bend and axial fatigue specimens were fabricated by NASA ULI collaborators across a range of scan velocities (800–2000 mm/s) at a constant power of 370 W using an EOS M290 system. All fatigue specimens were low-stress-ground by a commercial vendor and tested at Case Western Reserve University (CWRU) under load-controlled cyclic loading at a stress ratio of R = 0.1. This paper presents a curated dataset linking PBF-LB/M process parameters to fatigue outcomes across 175 specimens. Of these, 136 fractured and this study includes fatigue crack initiation site identification and defect morphology metrics derived from post mortem SEM analysis. Specimens that reached runout (10⁷ cycles) and did not fracture under subsequent fatigue testing are retained in the dataset, with fractographic fields marked as ‘NA’ to indicate non-applicability. The dataset includes specimen metadata, processing parameters, fatigue life data, fatigue initiation site classification (e.g., keyhole, gas-entrapped pore (GeP), lack-of-fusion (LoF), contamination), defect size and shape descriptors, and spatial location relative to the free surface. These data are intended to support defect-based fatigue life prediction, probabilistic modeling, process–structure–property studies, and machine learning frameworks linking process parameters to fatigue performance in PBF-LB/M Ti-6Al-4V. Full article

The effect of corporate ESG performance on firm competitiveness has attracted growing attention from both regulators and market participants. Most studies explore and interpret this effect from the perspective of supply-side factors such as technological innovation; however, the role of customer-side factors remains underexplored. This exploratory study aims to theoretically and empirically analyze the mediation role of the customer-side factors in the impact of corporate ESG on market share. Based on a review of the literature, we develop a theoretical model linking corporate ESG performance to customer purchase behavior. The derived hypotheses are empirically checked using panel data of Chinese listed companies from 2009 to 2023 using two-way fixed-effect regression, three-step mediation analysis, and Sobel test. The results show that the effect of ESG performance on market share is significantly positive, and this relationship is mediated by three variables: corporate reputation, firm visibility, and market coverage. Therefore, we suggest that (i) the Chinese government should strengthen mandatory ESG disclosure requirements and enhance supervision of ESG rating agencies; (ii) corporations should substantially improve their ESG performance and enhance ESG communication capabilities; (iii) customers should pay more attention to public interest, allowing individual benefits to align with social welfare, thereby achieving a win-win outcome for both customers and corporations. Full article

Climate change is intensifying hydroclimatic extremes and agricultural water scarcity, sharpening trade-offs among yield stability, water saving, and farm incomes in major grain regions. Existing studies often optimise cropping patterns or irrigation schedules separately, seldom embedding yield robustness and policy instruments in one decision framework. We propose an integrated Machine-learning–System-dynamics–Non-dominated-sorting-genetic-algorithm-II (ML–SD–NSGA-II) framework linking long-horizon meteorological scenario generation, crop–water–economy feedback and multi-objective optimisation of crop areas and irrigation depths. ML models generate daily climate sequences to drive an SD model of soil moisture, yield formation, basin-scale allocable water, and farm returns; NSGA-II searches Pareto-optimal strategies that maximise profit and irrigation water productivity while minimising yield deviation. Applied to a rice–wheat irrigation system in the middle Yangtze River Basin, knee-point solutions lift irrigation water productivity by about 14%, maintain near-baseline profits, and reduce yield deviation. Scenario tests with block tariffs, quota-based subsidies, and extreme drought show pricing mainly curbs low-value water use in normal years, while under drought, physical scarcity dominates and economic tools offer limited buffering. This reveals the existence of a scarcity-regime threshold beyond which economic instruments become second-order relative to binding biophysical constraints. The framework supports transparent ex ante testing of tariff–subsidy packages for irrigation governance and adaptation. Full article

Smart Specialization Strategy (S3) has become a central instrument of European Union Cohesion Policy, yet its implementation has revealed recurring limitations, including formalistic Entrepreneurial Discovery Processes, weak multilevel coordination, generic priorities, and evaluation systems focused mainly on innovation outputs. This paper examines how shared purpose can be incorporated into S3 in ways that improve both developmental direction and implementation quality across levels. The study adopts a conceptual research design based on a critical synthesis of literature and a model-building procedure, complemented by an illustrative regional application. The main result is the Purpose-Driven Smart Specialization (S3+P) framework, a multilevel model linking individual, organizational, territorial, and macro-policy dimensions through five catalytic mechanisms: plasticity, temporality, identity, memory, and relational networks. The paper also proposes a six-step policy cycle and an indicator logic that broadens evaluation beyond conventional innovation metrics. The analysis suggests that purpose can strengthen directionality, coherence, and legitimacy in regional strategy while preserving the place-based and discovery-oriented rationale of S3. The framework contributes to current debates on the renewal of smart specialization for more sustainable and coordinated regional development. Full article

Interactive digital commerce environments increasingly rely on influencers as algorithmically amplified intermediaries between brands and consumers. However, the process through which influencer attributes translate into brand trust remains theoretically underdeveloped. Drawing on Social Influence Theory and Source Credibility Theory, this study develops a process-based model in which consumer engagement operates as a psychological mechanism linking influencer characteristics, namely credibility, brand alignment, interactivity, and authenticity, to brand trust. Using survey data from 400 active social media users in Lebanon and partial least squares structural equation modeling (PLS-SEM), the findings reveal that all four influencer attributes significantly enhance consumer engagement, which in turn strongly predicts brand trust. Influencer–brand alignment emerges as the strongest driver of engagement, suggesting that value congruence functions as a heuristic cue in interactive digital commerce contexts. By conceptualizing engagement as a trust-internalization mechanism within platform-mediated environments, this study advances electronic commerce theory and provides context-sensitive insight into digital trust formation in emerging markets. Full article